Optimising the long term management of invasive species affecting biodiversity and the rural economy using adaptive management

大量具有高度破坏性的外来入侵物种（INNS）已在南美洲形成。它们影响本地物种、生态系统和生计。许多INNS现在如此普遍，根除是不可能的。在那些不采取行动是不可接受的地区，必须长期控制它们的传播，降低它们的密度。这必须尽可能节约成本，并考虑：应将INNS密度降低多少？这取决于可用于管理的资源，以及焦点生物毒素的丰富程度与其对人类和生物多样性造成的危害之间的关系。考虑到如果现在不采取行动，将来会造成什么伤害也很重要。如何达到预期的减排目标？种群中不同的个体对传播的贡献是不同的。因此，应该根据物种的生物学（例如，大松树比小松树产生更多的种子）来确定正确的年龄等级或在不同的季节采取行动。该物种应该在哪里减少？INNS入侵的地区往往是巨大的，空间优先排序是必要的。INNS在所有领域的破坏性并不相同。一些生态系统和人类活动可以承受低密度的生态系统，而另一些生态系统则非常脆弱，甚至无法忍受低密度的生态系统。一个例子是南巴塔哥尼亚极度濒危的连帽grebe，被引进的美国水貂逼到濒临灭绝。管理INNS的费用也因地区而异，特别是在南美洲，那里的一些地区很难进入，而且劳动力稀少。另一个重要的考虑因素是INNS是可移动的。当它们第一次入侵时，它们就能够扩散，并且可以重新入侵它们被驱散的地区。如果管理层能够利用已知的传播模式，这既是挑战也是机遇。几十年来，生态学家一直在详细研究扩散动力学，但很少利用这些知识来设计有效的管理干预措施。例如，可以通过从一个小而极具吸引力的地区集中清除一个流动的生态系统来耗尽它，从而经济有效地“真空”一个大得多的地区，或者可以通过有空间针对性的土地管理使种子不可能建立，从而遏制植物生态系统的传播。我们将设计并向利益相关者介绍一种用户友好的决策工具，我们预计该工具将在拉丁美洲得到广泛使用。为了确保我们的方法适用于拉丁美洲的不同情况，我们将研究具有较大影响的示例物种，并且已经存在相关数据（入侵松树，女贞和水貂）。我们还将对影响巴西、阿根廷和智利当地社区的缺乏数据的松树物种、外来草和食肉黄蜂的合理情景进行建模。我们将找到最有效的战略管理使用复杂的计算机模拟考虑物种生态，分散和干预成本在空间背景下。我们将确定新数据在哪些方面能够最有效地减少最佳行动方案的不确定性。我们解决的问题是复杂的，我们将把它嵌入到合作适应性管理的过程中，这样管理者就可以通过面对不同的模型和数据来不断提高他们的效率。我们还将把我们的项目作为在拉丁美洲建立研究能力的一种方式，通过研究访问、持续合作和研讨会的方式培训早期职业研究人员和博士生。我们的项目将对拉丁美洲的人民和生物多样性产生切实的积极和直接的影响，通过逐步改变有问题的国际生物识别系统的管理。

A large number of highly damaging invasive non-native species (INNS) have become established in South America. They affect native species, ecosystems and livelihoods. Many INNS are now so widespread that eradication is not an option. Their spread must be contained and their density reduced, in the long-term, in those areas where taking no action is not acceptable. This must be done as cost effectively as possible, and consider:By how much should INNS density be reduced? This depends on the resources available for management and on the relationship between the abundance of the focal INNS and the harm it causes to people and biodiversity. Considering what harm would be caused in the future if no action was taken now is also important.How should the desired reduction be achieved? Different individuals in a population contribute differently to spread. Thus, targeting the right age classes or acting in different seasons should be informed by the biology of the species (e.g. large pines produce more seeds than small ones).Where should the species be reduced? The areas invaded by INNS are often vast and spatial prioritisation is necessary. INNS are not equally damaging in all areas. Some ecosystems and human activities can withstand low density INNS presence, while others are so vulnerable they cannot tolerate even low INNS density. An example is the critically endangered hooded grebe in Austral Patagonia, driven to near extinction by the introduced American mink. The cost of managing INNS also varies spatially, especially in South America, where some areas are very difficult to access and the workforce is sparse. A further important consideration is that INNS are mobile. They have been able to spread when they first invaded, and can re-invade areas from which they have been removed through dispersal. This is both a challenge and an opportunity if management can exploit known patterns of spread. Ecologists have been studying dispersal dynamics in detail for decades, but have rarely used this knowledge to design effective management interventions. For instance, it may be possible to deplete a mobile INNS by intensively removing it from a small, highly attractive area, hence cost-effectively "vacuuming" a much larger area, or the spread of a plant INNS may be contained by making the establishment of seeds unlikely through spatially targeted land management.We will design and introduce to stakeholders a user-friendly decision tool that we expect will become widely used in Latin America. To make sure our approach is relevant for different contexts in Latin America, we will work with example species that have large impacts, and for which data already exist (invasive pines, privet, and mink). We will also model plausible scenarios for data-poor pine species, exotic grasses and carnivorous wasps, which impact local communities in Brazil, Argentina and Chile. We will find the most effective strategic management using sophisticated computer simulations considering species ecology, dispersal and intervention costs in a spatial context. We will identify where new data would most effectively reduce uncertainty on the best course of action. The problem we tackle is complex, and we will embed it in a process of co-operative adaptive management, so that managers continually improve their effectiveness by confronting different models to data. We will also use our project as a way to build research capacity in Latin America, by training early career researchers and PhD students by means of research visits, continuous collaboration and workshops.Our project will have a tangible positive and immediate impact on people and biodiversity in Latin America by delivering a step-change in the management of problematic INNS.